234,405
174,591 (98.2%)
3,252 (1.8%)
138,259,845
78,739,488 (96.4%)
2,972,617 (3.6%)
The Coronavirus Dashboard: the case of Ethiopia
This Coronavirus dashboard: the case of Ethiopia provides an overview of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic for Ethiopia. This dashboard is built with R using the R Makrdown framework and was adapted from this dashboard by Rami Krispin.
Code
The code behind this dashboard is available on GitHub.
Data
The input data for this dashboard is the dataset available from the {Data source} Github source. run the following code to get the latest dataset from raw material:
#------------------ Data ------------------
urlconfirmed="https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv"
data.confirmed <- read.csv(url(urlconfirmed), sep = "," )
urldeath="https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv"
data.deaths <- read.csv(url(urldeath), sep = ",")
urlrecovered="https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv"
data.recovered <- read.csv(url(urlrecovered), sep = ",")
The data and dashboard are refreshed on a daily basis.
The raw data is pulled from the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus
Contact
For any question or feedback, follow on Telegram | Facebook| Twitter|
---
title: "Corona virus in Ethiopia "
author: " Zenebe Ageru Yilma"
output:
flexdashboard::flex_dashboard:
orientation: rows
#social: ["facebook", "twitter", "linkedin"]
source_code: embed
vertical_layout: fill
---
```{r setup, include=FALSE }
#------------------ Packages ------------------#
library(flexdashboard)
library(tidyverse)
library(magrittr)
library(lubridate)
library(gridExtra)
library(kableExtra)
#install.packages("devtools")
#devtools::install_github("RamiKrispin/coronavirus")
#library(coronavirus)
#data(coronavirus)
#update_datasets()
# View(coronavirus)
#max(coronavirus$date)
`%>%` <- magrittr::`%>%`
#------------------ Parameters ------------------
# Set colors
# https://www.w3.org/TR/css-color-3/#svg-color
confirmed_color <- "purple"
active_color <- "#1f77b4"
recovered_color <- "forestgreen"
death_color <- "red"
#------------------ Data ------------------
urlconfirmed="https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv"
data.confirmed <- read.csv(url(urlconfirmed), sep = "," )
urldeath="https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv"
data.deaths <- read.csv(url(urldeath), sep = ",")
urlrecovered="https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv"
data.recovered <- read.csv(url(urlrecovered), sep = ",")
n.col <- ncol(data.confirmed)
data.confirmed %<>%pivot_longer(cols = 5:n.col, names_to = "date", values_to = "cases") %<>% mutate(date = date %>% substr(2,10) %>% mdy())%>%add_column(type = factor("confirmed"))
data.deaths %<>%pivot_longer(cols = 5:n.col, names_to = "date", values_to = "cases") %<>% mutate(date = date %>% substr(2,10) %>% mdy())%>%add_column(type = factor("death"))
data.recovered %<>%pivot_longer(cols = 5:n.col, names_to = "date", values_to = "cases") %<>% mutate(date = date %>% substr(2,10) %>%
mdy())%>%add_column(type = factor("recovered"))
coronavirus <- rbind(data.confirmed, data.deaths, data.recovered)
coronavirus$date <- as.Date(coronavirus$date,"%m/%d/%y")
coronavirus$Country.Region <- as.character(coronavirus$Country.Region)
coronavirus$Province.State<- as.character(coronavirus$Province.State)
df_w <- coronavirus %>%
dplyr::filter(date == max(date)) %>%
dplyr::group_by(Country.Region, type) %>%
dplyr::summarise(total = sum(cases)) %>%
tidyr::pivot_wider(
names_from = type,
values_from = total
) %>%
dplyr::mutate(unrecovered = confirmed - ifelse(is.na(recovered), 0, recovered) - ifelse(is.na(death), 0, death)) %>%
dplyr::mutate(unrecovered = confirmed - ifelse(is.na(death), 0, death)) %>%
dplyr::arrange(-confirmed) %>%
dplyr::ungroup() %>%
dplyr::mutate(country = dplyr::if_else(Country.Region == "United Arab Emirates", "UAE", Country.Region)) %>%
dplyr::mutate(country = dplyr::if_else(country == "Mainland China", "China", country)) %>%
dplyr::mutate(country = dplyr::if_else(country == "North Macedonia", "N.Macedonia", country)) %>%
dplyr::mutate(country = trimws(country)) %>%
dplyr::mutate(country = factor(country, levels = country))
dw_daily <- coronavirus %>%
dplyr::group_by(date, type) %>%
dplyr::summarise(total = sum(cases, na.rm = TRUE)) %>%
tidyr::pivot_wider(
names_from = type,
values_from = total
) %>%
dplyr::arrange(date) %>%
#dplyr::ungroup() %>%
dplyr::mutate(active = confirmed - death - recovered) %>%
dplyr::mutate(active = confirmed - death) %>%
dplyr::mutate(
confirmed_cum = cumsum(confirmed),
death_cum = cumsum(death),
recovered_cum = cumsum(recovered),
active_cum = cumsum(active)
)
df <- coronavirus %>%
dplyr::filter(date == max(date)) %>%
dplyr::filter(Country.Region == "Ethiopia") %>%
dplyr::group_by(Country.Region, type) %>%
dplyr::summarise(total = sum(cases)) %>%
tidyr::pivot_wider(
names_from = type,
values_from = total
) %>%
dplyr::mutate(unrecovered = confirmed - ifelse(is.na(recovered), 0, recovered) - ifelse(is.na(death), 0, death)) %>%
dplyr::mutate(unrecovered = confirmed - ifelse(is.na(death), 0, death)) %>%
dplyr::arrange(-confirmed) %>%
dplyr::ungroup() %>%
dplyr::mutate(country = dplyr::if_else(Country.Region == "United Arab Emirates", "UAE", Country.Region)) %>%
dplyr::mutate(country = dplyr::if_else(country == "Mainland China", "China", country)) %>%
dplyr::mutate(country = dplyr::if_else(country == "North Macedonia", "N.Macedonia", country)) %>%
dplyr::mutate(country = trimws(country)) %>%
dplyr::mutate(country = factor(country, levels = country))
df_daily <- coronavirus %>%
dplyr::filter(Country.Region == "Ethiopia") %>%
dplyr::filter(date>="2020-03-01") %>%
dplyr::group_by(date, type) %>%
dplyr::summarise(total = sum(cases, na.rm = TRUE)) %>%
tidyr::pivot_wider(
names_from = type,
values_from = total
) %>%
dplyr::arrange(date) %>%
#dplyr::ungroup() %>%
dplyr::mutate(active = confirmed - death - recovered) %>%
dplyr::mutate(active = confirmed - death) %>%
dplyr::mutate(
confirmed_cum = cumsum(confirmed),
death_cum = cumsum(death),
recovered_cum = cumsum(recovered),
active_cum = cumsum(active)
)
df1 <- coronavirus %>% dplyr::filter(date == max(date))
```
In Ethiopia
=======================================================================
Row {data-width=400}
-----------------------------------------------------------------------
### confirmed {.value-box}
```{r}
valueBox(
value = paste(format(sum(df$confirmed), big.mark = ","), "", sep = " "),
caption = "Total confirmed cases",
icon = "fas fa-user-md",
color = confirmed_color
)
```
### Recovered {.value-box}
```{r}
valueBox(
value = paste(format(sum(df$recovered, na.rm = TRUE), big.mark = ","), " (",
round(100 * sum(df$recovered, na.rm = TRUE) / sum(df$recovered+df$death), 1),
"%)",
sep = ""
),
caption = "Recovered cases (recovery rate)",
icon = "fas fa-heart-broken",
color = recovered_color
)
```
### death {.value-box}
```{r}
valueBox(
value = paste(format(sum(df$death, na.rm = TRUE), big.mark = ","), " (",
round(100 * sum(df$death, na.rm = TRUE) / sum(df$death+df$recovered), 1),
"%)",
sep = ""
),
caption = "Death cases (death rate)",
icon = "fas fa-heart-broken",
color = death_color
)
```
Row
-----------------------------------------------------------------------
### **Daily cumulative cases by type** (Ethiopia only)
```{r}
plotly::plot_ly(data = df_daily) %>%
plotly::add_trace(
x = ~date,
#y = ~active_cum,
y = ~confirmed_cum,
type = "scatter",
mode = "lines+markers",
#name = "Active",
name = "Confirmed",
line = list(color = active_color),
marker = list(color = active_color)
) %>%
plotly::add_trace(
x = ~date,
y = ~ recovered_cum,
type = "scatter",
mode = "lines+markers",
name = "Recovered",
line = list(color = recovered_color),
marker = list(color = recovered_color)
) %>%
plotly::add_trace(
x = ~date,
y = ~ death_cum,
type = "scatter",
mode = "lines+markers",
name = "Death",
line = list(color = death_color),
marker = list(color = death_color)
)%>%
plotly::add_annotations(
x = as.Date("2020-3-13"),
y = 1,
text = paste("First case"),
xref = "x",
yref = "y",
arrowhead = 5,
arrowhead = 3,
arrowsize = 1,
showarrow = TRUE,
ax = -10,
ay = -90
) %>%
plotly::add_annotations(
x = as.Date("2020-4-5"),
y = 1,
text = paste("First death"),
xref = "x",
yref = "y",
arrowhead = 5,
arrowhead = 3,
arrowsize = 1,
showarrow = TRUE,
ax = -10,
ay = -90
) %>%
plotly::add_annotations(
x = as.Date("2020-03-29"),
y = 14,
text = paste(
"New containment",
"",
"measures"
),
xref = "x",
yref = "y",
arrowhead = 5,
arrowhead = 3,
arrowsize = 1,
showarrow = TRUE,
ax = -10,
ay = -90
) %>%
plotly::layout(
title = "",
yaxis = list(title = "Cumulative number of cases"),
xaxis = list(title = "Date"),
legend = list(x = 0.1, y = 0.9),
hovermode = "compare"
)
```
Global
=======================================================================
Row {data-width=400}
-----------------------------------------------------------------------
### confirmed {.value-box}
```{r}
valueBox(
value = paste(format(sum(df_w$confirmed), big.mark = ","), "", sep = " "),
caption = "Total confirmed cases",
icon = "fas fa-user-md",
color = confirmed_color
)
```
### Recovered {.value-box}
```{r}
valueBox(
value = paste(format(sum(df_w$recovered, na.rm = TRUE), big.mark = ","), " (",
round(100 * sum(df_w$recovered, na.rm = TRUE) / sum(df_w$recovered +df_w$death), 1),
"%)",
sep = ""
),
caption = "Recovered cases (recovery rate)",
icon = "fas fa-heart-broken",
color = recovered_color
)
```
### death {.value-box}
```{r}
valueBox(
value = paste(format(sum(df_w$death, na.rm = TRUE), big.mark = ","), " (",
round(100 * sum(df_w$death, na.rm = TRUE) / sum(df_w$death+df_w$recovered), 1),
"%)",
sep = ""
),
caption = "Death cases (death rate)",
icon = "fas fa-heart-broken",
color = death_color
)
```
Row
-----------------------------------------------------------------------
### **Daily cumulative cases by type** (Global)
```{r}
plotly::plot_ly(data = dw_daily) %>%
plotly::add_trace(
x = ~date,
#y = ~active_cum,
y = ~confirmed_cum,
type = "scatter",
mode = "lines+markers",
#name = "Active",
name = "Confirmed",
line = list(color = active_color),
marker = list(color = active_color)
) %>%
plotly::add_trace(
x = ~date,
y = ~ recovered_cum,
type = "scatter",
mode = "lines+markers",
name = "Recovered",
line = list(color = recovered_color),
marker = list(color = recovered_color)
) %>%
plotly::add_trace(
x = ~date,
y = ~ death_cum,
type = "scatter",
mode = "lines+markers",
name = "Death",
line = list(color = death_color),
marker = list(color = death_color)
)%>%
plotly::layout(
title = "",
yaxis = list(title = "Cumulative number of cases"),
xaxis = list(title = "Date"),
legend = list(x = 0.1, y = 0.9),
hovermode = "compare"
)
```
Comparison
=======================================================================
Top ten
-----------------------------------------------------------------------
### **Cases distribution top ten countries**
```{r}
df_top <- coronavirus %>%
dplyr::filter(date == max(date)) %>%
dplyr::filter(Country.Region == "US" |
Country.Region == "Spain" |
Country.Region == "Chile"|
Country.Region == "United Kingdom" |
Country.Region =="Russia" |
Country.Region =="South Africa" |
Country.Region == "Peru"|
Country.Region == "Brazil"|
Country.Region == "Mexico"|
Country.Region =="India" ) %>%
dplyr::group_by(Country.Region, type) %>%
dplyr::summarise(total = sum(cases)) %>%
tidyr::pivot_wider(
names_from = type,
values_from = total
) %>%
# dplyr::mutate(unrecovered = confirmed - ifelse(is.na(recovered), 0, recovered) - ifelse(is.na(death), 0, death)) %>%
dplyr::mutate(unrecovered = confirmed - ifelse(is.na(death), 0, death)) %>%
dplyr::arrange(confirmed) %>%
dplyr::ungroup() %>%
dplyr::mutate(country = dplyr::if_else(Country.Region == "United Arab Emirates", "UAE", Country.Region)) %>%
dplyr::mutate(country = dplyr::if_else(country == "Mainland China", "China", country)) %>%
dplyr::mutate(country = dplyr::if_else(country == "North Macedonia", "N.Macedonia", country)) %>%
dplyr::mutate(country = trimws(country)) %>%
dplyr::mutate(country = factor(country, levels = country))
plotly::plot_ly(
data = df_top,
x = ~country,
# y = ~unrecovered,
y = ~ confirmed,
# text = ~ confirmed,
# textposition = 'auto',
type = "bar",
name = "Confirmed",
marker = list(color = active_color)
) %>%
plotly::add_trace(
y = ~recovered,
# text = ~ recovered,
# textposition = 'auto',
name = "Recovered",
marker = list(color = recovered_color)
) %>%
plotly::add_trace(
y = ~death,
# text = ~ death,
# textposition = 'auto',
name = "Death",
marker = list(color = death_color)
)%>%
plotly::layout(
barmode = "stack",
yaxis = list(title = "Total cases"),
xaxis = list(title = ""),
hovermode = "compare",
margin = list(
# l = 60,
# r = 40,
b = 10,
t = 10,
pad = 2
)
)
```
### **Cases distribution in Horn of Africa and Ethiopian Boarders**
```{r daily_summary}
df_hr <- coronavirus %>%
dplyr::filter(date == max(date)) %>%
dplyr::filter(Country.Region == "Ethiopia" |
Country.Region == "Djibouti" |
Country.Region == "Eritrea"|
Country.Region == "South Sudan"|
Country.Region == "Sudan"|
Country.Region == "Kenya"|
Country.Region == "Somalia") %>%
dplyr::group_by(Country.Region, type) %>%
dplyr::summarise(total = sum(cases)) %>%
tidyr::pivot_wider(
names_from = type,
values_from = total
) %>%
# dplyr::mutate(unrecovered = confirmed - ifelse(is.na(recovered), 0, recovered) - ifelse(is.na(death), 0, death)) %>%
dplyr::mutate(unrecovered = confirmed - ifelse(is.na(death), 0, death)) %>%
dplyr::arrange(confirmed) %>%
dplyr::ungroup() %>%
dplyr::mutate(country = dplyr::if_else(Country.Region == "United Arab Emirates", "UAE", Country.Region)) %>%
dplyr::mutate(country = dplyr::if_else(country == "Mainland China", "China", country)) %>%
dplyr::mutate(country = dplyr::if_else(country == "North Macedonia", "N.Macedonia", country)) %>%
dplyr::mutate(country = trimws(country)) %>%
dplyr::mutate(country = factor(country, levels = country))
plotly::plot_ly(
data = df_hr,
x = ~country,
# y = ~unrecovered,
y = ~ confirmed,
# text = ~ confirmed,
# textposition = 'auto',
type = "bar",
name = "Confirmed",
marker = list(color = active_color)
) %>%
plotly::add_trace(
y = ~death,
# text = ~ death,
# textposition = 'auto',
name = "Death",
marker = list(color = death_color)
) %>%
plotly::add_trace(
y = ~recovered,
# text = ~ recovered,
# textposition = 'auto',
name = "Recovered",
marker = list(color = recovered_color)
) %>%
plotly::layout(
barmode = "stack",
yaxis = list(title = "Total cases"),
xaxis = list(title = ""),
hovermode = "compare",
margin = list(
# l = 60,
# r = 40,
b = 10,
t = 10,
pad = 2
)
)
```
Daily New cases
=======================================================================
Row {data-width=400}
-------------------------------------
### ** top 5 Daily new cases **
```{r}
daily_confirmed <- coronavirus %>%
dplyr::filter(type == "confirmed") %>%
dplyr::mutate(country = Country.Region) %>%
dplyr::group_by(date, country) %>%
dplyr::summarise(total = sum(cases)) %>%
dplyr::ungroup() %>%
tidyr::pivot_wider(names_from = country, values_from = total)
#----------------------------------------
# Plotting the data
daily_confirmed %>%
plotly::plot_ly() %>%
plotly::add_trace(
x = ~date,
y = ~US,
type = "scatter",
mode = "lines+markers",
name = "USA"
) %>%
plotly::add_trace(
x = ~date,
y = ~Brazil,
type = "scatter",
mode = "lines+markers",
name = "Brazil"
) %>%
plotly::add_trace(
x = ~date,
y = ~India,
type = "scatter",
mode = "lines+markers",
name = "India"
) %>%
plotly::add_trace(
x = ~date,
y = ~ `South Africa`,
type = "scatter",
mode = "lines+markers",
name = "South Africa"
) %>%
plotly::add_trace(
x = ~date,
y = ~Russia,
type = "scatter",
mode = "lines+markers",
name = "Russia"
) %>%
plotly::layout(
title = "",
legend = list(x = 0.1, y = 0.9),
yaxis = list(title = "Number of new cases"),
xaxis = list(title = "Date"),
# paper_bgcolor = "black",
# plot_bgcolor = "black",
# font = list(color = 'white'),
hovermode = "compare",
margin = list(
# l = 60,
# r = 40,
b = 10,
t = 10,
pad = 2
)
)
```
-------------------------------------
### **Daily new cases in the Horn of Africa**
```{r}
daily_confirmed <- coronavirus %>%
dplyr::filter(type == "confirmed") %>%
dplyr::filter(date >= "2020-03-10") %>%
dplyr::mutate(country = Country.Region) %>%
dplyr::group_by(date, country) %>%
dplyr::summarise(total = sum(cases)) %>%
dplyr::ungroup() %>%
tidyr::pivot_wider(names_from = country, values_from = total)
#----------------------------------------
# Plotting the data
daily_confirmed %>%
plotly::plot_ly() %>%
plotly::add_trace(
x = ~date,
y = ~Ethiopia,
type = "scatter",
mode = "lines+markers",
name = "Ethiopia"
) %>%
plotly::add_trace(
x = ~date,
y = ~Djibouti,
type = "scatter",
mode = "lines+markers",
name = "Djibouti"
) %>%
plotly::add_trace(
x = ~date,
y = ~Somalia,
type = "scatter",
mode = "lines+markers",
name = "Somalia"
) %>%
plotly::add_trace(
x = ~date,
y = ~Sudan,
type = "scatter",
mode = "lines+markers",
name = "Sudan"
) %>%
plotly::add_trace(
x = ~date,
y = ~`South Sudan`,
type = "scatter",
mode = "lines+markers",
name = "South Sudan"
) %>%
plotly::add_trace(
x = ~date,
y = ~Kenya,
type = "scatter",
mode = "lines+markers",
name = "Kenya"
) %>%
plotly::add_trace(
x = ~date,
y = ~Eritrea,
type = "scatter",
mode = "lines+markers",
name = "Eritrea"
) %>%
plotly::layout(
title = "",
legend = list(x = 0.1, y = 0.9),
yaxis = list(title = "Number of new cases"),
xaxis = list(title = "Date"),
# paper_bgcolor = "black",
# plot_bgcolor = "black",
# font = list(color = 'white'),
hovermode = "compare",
margin = list(
# l = 60,
# r = 40,
b = 10,
t = 10,
pad = 2
)
)
```
Map
=======================================================================
### **World map of cases** (*use + and - icons to zoom in/out*)
```{r}
# map tab added by Art Steinmetz
library(leaflet)
library(leafpop)
library(purrr)
cv_data_for_plot <- coronavirus %>%
dplyr::filter(date == max(date)) %>%
dplyr::filter(cases > 0) %>%
dplyr::group_by(Country.Region, Province.State, Lat, Long, type) %>%
dplyr::summarise(cases = sum(cases)) %>%
dplyr::mutate(log_cases = 2 * log(cases)) %>%
dplyr::ungroup()
cv_data_for_plot.split <- cv_data_for_plot %>% split(cv_data_for_plot$type)
pal <- colorFactor(c("orange", "red", "green"), domain = c("confirmed", "death", "recovered"))
map_object <- leaflet() %>% addProviderTiles(providers$Stamen.Toner)
names(cv_data_for_plot.split) %>%
purrr::walk(function(df) {
map_object <<- map_object %>%
addCircleMarkers(
data = cv_data_for_plot.split[[df]],
lng = ~Long, lat = ~Lat,
# label=~as.character(cases),
color = ~ pal(type),
stroke = FALSE,
fillOpacity = 0.8,
radius = ~log_cases,
popup = leafpop::popupTable(cv_data_for_plot.split[[df]],
feature.id = FALSE,
row.numbers = FALSE,
zcol = c("type", "cases", "Country.Region", "Province.State")
),
group = df,
# clusterOptions = markerClusterOptions(removeOutsideVisibleBounds = F),
labelOptions = labelOptions(
noHide = F,
direction = "auto"
)
)
})
map_object %>%
addLayersControl(
overlayGroups = names(cv_data_for_plot.split),
options = layersControlOptions(collapsed = FALSE)
)
```
About
=======================================================================
**The Coronavirus Dashboard: the case of Ethiopia**
This Coronavirus dashboard: the case of Ethiopia provides an overview of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic for Ethiopia. This dashboard is built with R using the R Makrdown framework and was adapted from this [dashboard](https://ramikrispin.github.io/coronavirus_dashboard/){target="_blank"} by Rami Krispin.
**Code**
The code behind this dashboard is available on [GitHub](https://github.com/AntoineSoetewey/coronavirus_dashboard){target="_blank"}.
**Data**
The input data for this dashboard is the dataset available from the [`{Data source}`](https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_time_series){target="_blank"} Github source. run the following code to get the latest dataset from raw material:
```
#------------------ Data ------------------
urlconfirmed="https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv"
data.confirmed <- read.csv(url(urlconfirmed), sep = "," )
urldeath="https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv"
data.deaths <- read.csv(url(urldeath), sep = ",")
urlrecovered="https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv"
data.recovered <- read.csv(url(urlrecovered), sep = ",")
```
The data and dashboard are refreshed on a daily basis.
The raw data is pulled from the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus
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